mlr_learners_surv.dnnsurv: Survival DNNSurv Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Survival DNNSurv Learner

Description

Fits a neural network based on pseudo-conditional survival probabilities. Calls survivalmodels::dnnsurv() from package 'survivalmodels'.

Details

Custom nets can be used in this learner either using the survivalmodels::build_keras_net utility function or using keras. The number of output channels should be of length 1 and number of input channels is the number of features plus number of cuts.

Prediction types

This learner returns two prediction types:

1. distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using the internal survivalmodels::predict.dnnsurv() function.

2. crank: the expected mortality using survivalmodels::surv_to_risk().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.dnnsurv")

Meta Information

• Task type: “surv”

• Predict Types: “crank”, “distr”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3proba, mlr3extralearners, survivalmodels, keras, pseudo, tensorflow, distr6

Parameters

Id	Type	Default	Levels	Range
cuts	integer	5		[1, \infty)
cutpoints	untyped	-		-
custom_model	untyped	-		-
optimizer	character	adam	adadelta, adagrad, adamax, adam, nadam, rmsprop, sgd	-
lr	numeric	0.02		[0, \infty)
beta_1	numeric	0.9		[0, 1]
beta_2	numeric	0.999		[0, 1]
epsilon	numeric	-		[0, \infty)
decay	numeric	0		[0, \infty)
clipnorm	numeric	-		(-\infty, \infty)
clipvalue	numeric	-		(-\infty, \infty)
momentum	numeric	0		[0, \infty)
nesterov	logical	FALSE	TRUE, FALSE	-
loss_weights	untyped	-		-
weighted_metrics	untyped	-		-
early_stopping	logical	FALSE	TRUE, FALSE	-
min_delta	numeric	0		[0, \infty)
patience	integer	0		[0, \infty)
verbose	integer	0		[0, 2]
baseline	numeric	-		(-\infty, \infty)
restore_best_weights	logical	FALSE	TRUE, FALSE	-
batch_size	integer	32		[1, \infty)
epochs	integer	10		[1, \infty)
validation_split	numeric	0		[0, 1]
shuffle	logical	TRUE	TRUE, FALSE	-
sample_weight	untyped	-		-
initial_epoch	integer	0		[0, \infty)
steps_per_epoch	integer	-		[1, \infty)
validation_steps	integer	-		[1, \infty)
steps	integer	-		[0, \infty)
callbacks	untyped	-		-
rho	numeric	0.95		(-\infty, \infty)
global_clipnorm	numeric	-		(-\infty, \infty)
use_ema	logical	-	TRUE, FALSE	-
ema_momentum	numeric	0.99		(-\infty, \infty)
ema_overwrite_frequency	numeric	-		(-\infty, \infty)
jit_compile	logical	TRUE	TRUE, FALSE	-
initial_accumultator_value	numeric	0.1		(-\infty, \infty)
amsgrad	logical	FALSE	TRUE, FALSE	-
lr_power	numeric	-0.5		(-\infty, \infty)
l1_regularization_strength	numeric	0		[0, \infty)
l2_regularization_strength	numeric	0		[0, \infty)
l2_shrinkage_regularization_strength	numeric	0		[0, \infty)
beta	numeric	0		(-\infty, \infty)
centered	logical	FALSE	TRUE, FALSE	-

Installation

Package 'survivalmodels' is not on CRAN and has to be install from GitHub via remotes::install_github("RaphaelS1/survivalmodels").

Initial parameter values

• verbose is initialized to 0.

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvDNNSurv

Methods

Public methods

• LearnerSurvDNNSurv$new()

• LearnerSurvDNNSurv$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerSurvDNNSurv$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvDNNSurv$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

RaphaelS1

References

Zhao, Lili, Feng, Dai (2019). “Dnnsurv: Deep neural networks for survival analysis using pseudo values.” arXiv preprint arXiv:1908.02337.

See Also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Examples

lrn("surv.dnnsurv")

mlr-org/mlr3extralearners documentation built on Dec. 21, 2024, 2:21 p.m.